Description: Tree Canopy is defined as vegetation 8 feet tall or higher. Montgomery Planning regularly purchases LiDAR data to support various products, and the interactive tool you will see below is is one such product. Four band infrared imagery is combined with LiDAR to extract our tree canopy layers. The 2009, 2014, and 2018 tree canopy layers used the "QL2" LiDAR standard, which captures only 4 samples per square meter. Starting with the department’s 2020 tree canopy layers, a higher quality LiDAR standard called "QL1" was used. This new technology captures 8 samples per square meter. The new QL1 technology is better able to capture smaller saplings that the QL2 technology could have have missed. QL1 also has the ability to better detect the fringes of tree canopy. Because of the higher resolution of QL1, the overall tree canopy capture for any given area is higher than it would be if it were measured by the older QL2 today. For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620
Copyright Text: Information Technology & Innovation (ITI), Montgomery County Planning Department, MNCPPC
University of Vermont Spatial Lab
Description: This layer is a high-resolution tree canopy change-detection layer for Prince George's and Montgomery Counties, Maryland. It contains three tree-canopy classes for the period 2014-2018: (1) No Change; (2) Gain; and (3) Loss. It was created by mapping the change from the source LiDAR and imagery for the two time periods. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed, felled in storms, or canopy to disease were assigned to the Loss class. New tree canopy, either the result of natural growth or new plantings was assigned to the Gain class . Change was mapped using object-based image analysis (OBIA) techniques and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs) for the two time periods. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to a detailed manual review and correction. No minimum mapping unit was enforced. All detectable tree canopy was retained in the dataset.
Copyright Text: The University of Vermont Spatial Analysis Laboratory created this datasets in collaboration with Sanborn.
Description: This layer is a high-resolution tree canopy change-detection layer for Prince George's and Montgomery Counties, Maryland. It contains three tree-canopy classes for the period 2014-2018: (1) No Change; (2) Gain; and (3) Loss. It was created by mapping the change from the source LiDAR and imagery for the two time periods. Tree canopy that existed during both time periods was assigned to the No Change category while trees removed, felled in storms, or canopy to disease were assigned to the Loss class. New tree canopy, either the result of natural growth or new plantings was assigned to the Gain class . Change was mapped using object-based image analysis (OBIA) techniques and included similar source datasets (LiDAR-derived surface models, multispectral imagery, and thematic GIS inputs) for the two time periods. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to a detailed manual review and correction. No minimum mapping unit was enforced. All detectable tree canopy was retained in the dataset.
Copyright Text: The University of Vermont Spatial Analysis Laboratory created this datasets in collaboration with Sanborn.
Description: The geographic boundaries of Montgomery County's incorporated City, Towns or Villages.In Montgomery County, municipalities are divided by whether a jurisdiction has zoning authority or not. Those with zoning authority include, Barnesville, Brookeville, Gaithersburg, Laytonsville, Poolesville, Rockville and Washington Grove. The rest of the municipalities abide by the Montgomery County Planning Department zoning code.12/17/18: Kensignton Annexation Resolution No. AR-01-2018
Copyright Text: For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620